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Deciphering Cell Fate and Clonal Dynamics via Integrative Single-Cell Lineage Modeling
Yuntian Fu1, Divij Mathew2,3,4, Mingshuang Wang5
1Graduate Program in Genomics and Computational Biology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
Biorxiv : the Preprint Server for Biology
|September 15, 2025
Summary
Clonotrace integrates gene expression and lineage barcode data to precisely map cell development. This computational framework enhances the study of cell fate decisions and differentiation dynamics in various biological systems.
Area of Science:
- Single-cell biology
- Computational biology
- Immunology
Background:
- Single-cell technologies allow joint measurement of molecular states and clonal identities using lineage barcodes.
- Current computational methods for cell differentiation inference primarily use transcriptional similarity, neglecting lineage information.
- T-cell analysis is limited by subtle transcriptional differences, despite TCR sequencing providing clonal barcodes.
Purpose of the Study:
- To develop a computational framework, Clonotrace, that jointly models gene expression and clonotype information.
- To improve the inference of cell state transitions and fate biases with higher fidelity.
- To provide a broadly applicable tool for lineage-barcoded single-cell datasets, particularly for T-cell populations.
Main Methods:
- Developed Clonotrace, a computational framework integrating gene expression and clonotype data.
- Applied the framework to diverse systems including T cells, hematopoietic differentiation, and cancer models.
- Utilized lineage barcodes and single-cell RNA sequencing with TCR sequencing.
Main Results:
- Clonotrace infers cell state transitions and fate biases with higher fidelity than methods relying solely on transcriptional similarity.
- The framework successfully revealed differentiation hierarchies and distinguished unipotent from multipotent states across various systems.
- Identified candidate fate-determining genes driving lineage commitment in T cells and other cell types.
Conclusions:
- Clonotrace offers a significant advancement in analyzing lineage-barcoded single-cell data, particularly for T-cell responses in clinical and immunotherapy settings.
- The framework's ability to integrate molecular and clonal information provides deeper insights into cell fate and dynamics.
- Clonotrace is broadly applicable to diverse biological systems, advancing our understanding of differentiation and cell commitment.
